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IV : Prescriptive Analytics and Optimization
Welcome
Syllabus
I : Data Fundamentals and Preparation
Foundations of Data
1
Basic Concepts of Data
2
Structured, Semi-structured and Unstructured Data
3
Data in Organizations
4
Big Data
Data for Decision Making and Preparation
5
Role of Data in Decision Making
6
Data Types by Levels of Measurement
7
Meaning and Rationale of Data Analysis
8
Data Preparation: Cleaning, Munging, Normalization and Transformation
II : Exploratory and Confirmatory Data Analysis
Types of Data Analysis
9
Descriptive Data Analysis
10
Exploratory Data Analysis
11
Confirmatory Data Analysis
Analysis Tools with R
12
Univariate Data Analysis with R
13
Bivariate Data Analysis with R
14
Multivariate Data Analysis Tools
III : Predictive Analytics and Modeling Techniques
Methods I: Regression Techniques
15
Multiple Regression
16
Logistic Regression
17
Mediation Analysis
18
Moderation Analysis
19
Implementation of Methods with R
Methods II: Multivariate Analysis
20
Factor Analysis
21
Cluster Analysis
22
Implementation of Advanced Methods with R
IV : Prescriptive Analytics and Optimization
Linear and Integer Optimization
23
Linear Programming
24
Integer Programming
Network Optimization and Simulation
25
Network Optimization
26
Simulation Modelling
References
Contents
Foundations of Prescriptive Analytics
Optimization and Simulation Methods
IV : Prescriptive Analytics and Optimization
Foundations of Prescriptive Analytics
Introduction to Prescriptive Analytics
Linear Programming
Optimization and Simulation Methods
Integer Programming
Network Optimization
Simulation Modelling
22
Implementation of Advanced Methods with R
23
Linear Programming